import tensorflow as tf
from keras.layers.core import Dense, Dropout
from keras.layers import BatchNormalization
from keras.optimizers import Adam, SGD, Nadam
from keras.callbacks import ModelCheckpoint
from keras.models import Model, load_model
from tensorflow.keras.applications import densenet
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img, img_to_array
from keras import backend as K
import numpy as np
import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
#Load data
nTest = 90
nPixels = 224
mds_360 = np.loadtxt("mds_360.txt")
categories = [i for i in range(30) for j in range(12)]
def load_images(directory, nPixels, preprocesser):
X = []
for subdir, dirs, files in os.walk(directory):
for file in files:
if file.endswith(".jpg"):
img = load_img(os.path.join(subdir, file), target_size=(nPixels, nPixels))
x = img_to_array(img)
X.append(x)
X = np.stack(X)
X = preprocesser(X)
return X
X = load_images("360 Rocks", nPixels, lambda x: densenet.preprocess_input(np.expand_dims(x, axis=0)).squeeze())
(X_train_, X_test,
Y_train_, Y_test,
categories_train_, categories_test) = train_test_split(X,
mds_360,
categories,
test_size=nTest,
stratify=categories,
random_state=0)
(X_train, X_validate,
Y_train, Y_validate) = train_test_split(X_train_,
Y_train_,
test_size=nTest,
stratify=categories_train_,
random_state=0)
X_120 = load_images("120 Rocks", nPixels, lambda x: densenet.preprocess_input(np.expand_dims(x, axis=0)).squeeze())
Y_120 = np.loadtxt("mds_120.txt")
datagen = ImageDataGenerator(featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
channel_shift_range=0.,
fill_mode='nearest',
cval=0.,
horizontal_flip=True,
vertical_flip=True)
from keras.callbacks import Callback
class CustomEarlyStopping(Callback):
def __init__(self, filepath, monitor='val_loss', patience=20, warmup_epochs=5):
super(CustomEarlyStopping, self).__init__()
self.filepath = filepath
self.monitor = monitor
self.patience = patience
self.warmup_epochs = warmup_epochs
self.best_weights = None
self.wait = 0
self.stopped_epoch = 0
self.best_val_loss = float('inf')
def on_epoch_end(self, epoch, logs=None):
current_val_loss = logs.get(self.monitor)
if current_val_loss is None:
return
if epoch < self.warmup_epochs:
# If still in warmup phase, do not trigger early stopping
return
if current_val_loss < self.best_val_loss:
self.best_val_loss = current_val_loss
self.wait = 0
# Save the weights of the best model
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience:
self.stopped_epoch = epoch
self.model.set_weights(self.best_weights) # Load best weights
self.model.save(self.filepath) # Save the best model to a file
self.model.stop_training = True # Stop training
def on_train_end(self, logs=None):
if self.stopped_epoch > 0:
print(f"Epoch {self.stopped_epoch + 1}: early stopping")
else:
print("Training completed without early stopping")
nDim = 8
nEpochs = 500
dropout = 0.5
nEnsemble = 10
nDense = 256
nLayers = 2
loglr = -2.2200654426745987
lr = 10 ** loglr
batch_size = 90
for e in range(nEnsemble):
#Build model
arch = densenet.DenseNet121(include_top=False, pooling='avg')
for layer in arch.layers:
layer.trainable = False
x = arch.output
x = Dropout(dropout)(x)
for lyr in range(nLayers):
x = Dense(nDense, activation='relu', kernel_initializer='he_normal')(x)
x = BatchNormalization()(x)
x = Dropout(dropout)(x)
x = Dense(nDim)(x)
model = Model(inputs=arch.input, outputs=x)
#Initial training
model.compile(loss='mean_squared_error', optimizer=Nadam(learning_rate=lr))
filepath = 'intermediate_model_densenet_500_256_2.hdf5'
custom_early_stopping = CustomEarlyStopping(filepath, monitor='val_loss', patience=20, warmup_epochs=50)
hist1 = model.fit(datagen.flow(X_train, Y_train, batch_size),
steps_per_epoch=len(X_train) / batch_size,
epochs=nEpochs,
validation_data=(X_validate, Y_validate),
callbacks=[custom_early_stopping],
verbose=True)
#Fine tuning
model = load_model("intermediate_model_densenet_500_256_2.hdf5")
for layer in model.layers:
layer.trainable = True
model.compile(optimizer=Nadam(learning_rate=0.0001), loss='mean_squared_error')
batch_size = 30 #reduce the batch size so that the gradients of all layers can fit in memory
filepath = 'ensemble_densenet_8_{}.hdf5'.format(e)
custom_early_stopping = CustomEarlyStopping(filepath, monitor='val_loss', patience=20)
hist2 = model.fit(datagen.flow(X_train, Y_train, batch_size),
steps_per_epoch=len(X_train) / batch_size,
epochs=nEpochs,
validation_data=(X_validate, Y_validate),
callbacks=[custom_early_stopping],
verbose=True)
K.clear_session() #Clear tensorflow session to prevent memory issues
Epoch 1/500 2/2 [==============================] - 11s 3s/step - loss: 9.8876 - val_loss: 6.7203 Epoch 2/500 2/2 [==============================] - 2s 1s/step - loss: 9.1201 - val_loss: 6.5210 Epoch 3/500 2/2 [==============================] - 2s 1s/step - loss: 7.6253 - val_loss: 6.5451 Epoch 4/500 2/2 [==============================] - 2s 1s/step - loss: 7.1695 - val_loss: 6.9089 Epoch 5/500 2/2 [==============================] - 2s 1s/step - loss: 6.7570 - val_loss: 7.0188 Epoch 6/500 2/2 [==============================] - 2s 1s/step - loss: 6.1269 - val_loss: 7.5409 Epoch 7/500 2/2 [==============================] - 2s 1s/step - loss: 5.8250 - val_loss: 7.2855 Epoch 8/500 2/2 [==============================] - 2s 1s/step - loss: 5.5841 - val_loss: 8.0342 Epoch 9/500 2/2 [==============================] - 2s 1s/step - loss: 5.3348 - val_loss: 8.3533 Epoch 10/500 2/2 [==============================] - 2s 1s/step - loss: 4.9806 - val_loss: 8.1148 Epoch 11/500 2/2 [==============================] - 2s 1s/step - loss: 4.8408 - val_loss: 7.8814 Epoch 12/500 2/2 [==============================] - 2s 1s/step - loss: 4.6797 - val_loss: 6.1779 Epoch 13/500 2/2 [==============================] - 2s 1s/step - loss: 4.6326 - val_loss: 5.9979 Epoch 14/500 2/2 [==============================] - 2s 1s/step - loss: 4.4477 - val_loss: 5.7529 Epoch 15/500 2/2 [==============================] - 2s 1s/step - loss: 4.2265 - val_loss: 6.0981 Epoch 16/500 2/2 [==============================] - 2s 1s/step - loss: 4.3330 - val_loss: 5.9041 Epoch 17/500 2/2 [==============================] - 2s 1s/step - loss: 3.8968 - val_loss: 6.1827 Epoch 18/500 2/2 [==============================] - 2s 1s/step - loss: 3.9614 - val_loss: 6.2283 Epoch 19/500 2/2 [==============================] - 2s 1s/step - loss: 3.8383 - val_loss: 5.6413 Epoch 20/500 2/2 [==============================] - 2s 1s/step - loss: 3.7839 - val_loss: 5.1788 Epoch 21/500 2/2 [==============================] - 2s 1s/step - loss: 3.8042 - val_loss: 5.0101 Epoch 22/500 2/2 [==============================] - 2s 1s/step - loss: 3.7784 - val_loss: 5.3462 Epoch 23/500 2/2 [==============================] - 2s 1s/step - loss: 3.6717 - val_loss: 4.9919 Epoch 24/500 2/2 [==============================] - 2s 1s/step - loss: 3.4442 - val_loss: 4.6811 Epoch 25/500 2/2 [==============================] - 2s 1s/step - loss: 3.3765 - val_loss: 4.4233 Epoch 26/500 2/2 [==============================] - 2s 1s/step - loss: 3.4121 - val_loss: 4.3387 Epoch 27/500 2/2 [==============================] - 2s 1s/step - loss: 3.2221 - val_loss: 4.0075 Epoch 28/500 2/2 [==============================] - 2s 1s/step - loss: 3.3416 - val_loss: 3.9367 Epoch 29/500 2/2 [==============================] - 2s 1s/step - loss: 3.2523 - val_loss: 3.9308 Epoch 30/500 2/2 [==============================] - 2s 1s/step - loss: 3.1732 - val_loss: 4.0979 Epoch 31/500 2/2 [==============================] - 2s 1s/step - loss: 3.3929 - val_loss: 3.7054 Epoch 32/500 2/2 [==============================] - 2s 1s/step - loss: 3.0056 - val_loss: 3.7024 Epoch 33/500 2/2 [==============================] - 2s 1s/step - loss: 3.2407 - val_loss: 3.6322 Epoch 34/500 2/2 [==============================] - 2s 1s/step - loss: 3.0613 - val_loss: 3.5949 Epoch 35/500 2/2 [==============================] - 2s 1s/step - loss: 2.9829 - val_loss: 3.6115 Epoch 36/500 2/2 [==============================] - 2s 1s/step - loss: 3.0758 - val_loss: 3.6988 Epoch 37/500 2/2 [==============================] - 2s 1s/step - loss: 2.9311 - val_loss: 3.5999 Epoch 38/500 2/2 [==============================] - 2s 1s/step - loss: 3.0015 - val_loss: 3.5074 Epoch 39/500 2/2 [==============================] - 2s 1s/step - loss: 2.9840 - val_loss: 3.3224 Epoch 40/500 2/2 [==============================] - 2s 1s/step - loss: 2.9746 - val_loss: 3.2716 Epoch 41/500 2/2 [==============================] - 2s 1s/step - loss: 2.9238 - val_loss: 3.1033 Epoch 42/500 2/2 [==============================] - 2s 1s/step - loss: 2.8859 - val_loss: 3.1538 Epoch 43/500 2/2 [==============================] - 2s 1s/step - loss: 2.7876 - val_loss: 3.0500 Epoch 44/500 2/2 [==============================] - 2s 1s/step - loss: 2.7502 - val_loss: 3.0135 Epoch 45/500 2/2 [==============================] - 2s 1s/step - loss: 2.8434 - val_loss: 3.0481 Epoch 46/500 2/2 [==============================] - 2s 1s/step - loss: 2.7447 - val_loss: 3.0670 Epoch 47/500 2/2 [==============================] - 2s 1s/step - loss: 2.6469 - val_loss: 2.9782 Epoch 48/500 2/2 [==============================] - 2s 1s/step - loss: 2.7360 - val_loss: 2.7679 Epoch 49/500 2/2 [==============================] - 2s 1s/step - loss: 2.4993 - val_loss: 2.7643 Epoch 50/500 2/2 [==============================] - 2s 1s/step - loss: 2.8166 - val_loss: 2.6442 Epoch 51/500 2/2 [==============================] - 2s 1s/step - loss: 2.7725 - val_loss: 2.6729 Epoch 52/500 2/2 [==============================] - 2s 1s/step - loss: 2.5818 - val_loss: 2.7519 Epoch 53/500 2/2 [==============================] - 2s 1s/step - loss: 2.7116 - val_loss: 2.7584 Epoch 54/500 2/2 [==============================] - 2s 1s/step - loss: 2.7068 - val_loss: 2.7133 Epoch 55/500 2/2 [==============================] - 2s 1s/step - loss: 2.5763 - val_loss: 2.6509 Epoch 56/500 2/2 [==============================] - 2s 1s/step - loss: 2.7274 - val_loss: 2.6626 Epoch 57/500 2/2 [==============================] - 2s 1s/step - loss: 2.6448 - val_loss: 2.5227 Epoch 58/500 2/2 [==============================] - 2s 1s/step - loss: 2.5801 - val_loss: 2.5245 Epoch 59/500 2/2 [==============================] - 2s 1s/step - loss: 2.6952 - val_loss: 2.4284 Epoch 60/500 2/2 [==============================] - 2s 1s/step - loss: 2.5499 - val_loss: 2.3811 Epoch 61/500 2/2 [==============================] - 2s 1s/step - loss: 2.7612 - val_loss: 2.4189 Epoch 62/500 2/2 [==============================] - 2s 1s/step - loss: 2.6554 - val_loss: 2.4126 Epoch 63/500 2/2 [==============================] - 2s 1s/step - loss: 2.4961 - val_loss: 2.4235 Epoch 64/500 2/2 [==============================] - 2s 1s/step - loss: 2.4933 - val_loss: 2.3651 Epoch 65/500 2/2 [==============================] - 3s 1s/step - loss: 2.6575 - val_loss: 2.4129 Epoch 66/500 2/2 [==============================] - 2s 1s/step - loss: 2.6261 - val_loss: 2.4089 Epoch 67/500 2/2 [==============================] - 2s 1s/step - loss: 2.5414 - val_loss: 2.3941 Epoch 68/500 2/2 [==============================] - 2s 1s/step - loss: 2.5936 - val_loss: 2.3843 Epoch 69/500 2/2 [==============================] - 3s 1s/step - loss: 2.4665 - val_loss: 2.3144 Epoch 70/500 2/2 [==============================] - 3s 1s/step - loss: 2.6269 - val_loss: 2.3091 Epoch 71/500 2/2 [==============================] - 2s 1s/step - loss: 2.7151 - val_loss: 2.3713 Epoch 72/500 2/2 [==============================] - 2s 1s/step - loss: 2.4273 - val_loss: 2.2955 Epoch 73/500 2/2 [==============================] - 2s 1s/step - loss: 2.4347 - val_loss: 2.2411 Epoch 74/500 2/2 [==============================] - 3s 1s/step - loss: 2.4052 - val_loss: 2.2130 Epoch 75/500 2/2 [==============================] - 2s 1s/step - loss: 2.4492 - val_loss: 2.2198 Epoch 76/500 2/2 [==============================] - 2s 1s/step - loss: 2.4417 - val_loss: 2.2499 Epoch 77/500 2/2 [==============================] - 2s 1s/step - loss: 2.6880 - val_loss: 2.2494 Epoch 78/500 2/2 [==============================] - 2s 1s/step - loss: 2.6203 - val_loss: 2.1961 Epoch 79/500 2/2 [==============================] - 2s 1s/step - loss: 2.5055 - val_loss: 2.1587 Epoch 80/500 2/2 [==============================] - 2s 1s/step - loss: 2.6379 - val_loss: 2.1593 Epoch 81/500 2/2 [==============================] - 2s 1s/step - loss: 2.3890 - val_loss: 2.1611 Epoch 82/500 2/2 [==============================] - 2s 1s/step - loss: 2.4643 - val_loss: 2.1857 Epoch 83/500 2/2 [==============================] - 2s 1s/step - loss: 2.4571 - val_loss: 2.1829 Epoch 84/500 2/2 [==============================] - 2s 1s/step - loss: 2.5958 - val_loss: 2.2314 Epoch 85/500 2/2 [==============================] - 2s 1s/step - loss: 2.5712 - val_loss: 2.2705 Epoch 86/500 2/2 [==============================] - 2s 1s/step - loss: 2.3978 - val_loss: 2.2509 Epoch 87/500 2/2 [==============================] - 2s 1s/step - loss: 2.3232 - val_loss: 2.2174 Epoch 88/500 2/2 [==============================] - 2s 1s/step - loss: 2.3527 - val_loss: 2.2123 Epoch 89/500 2/2 [==============================] - 2s 1s/step - loss: 2.5500 - val_loss: 2.2253 Epoch 90/500 2/2 [==============================] - 2s 1s/step - loss: 2.4930 - val_loss: 2.1736 Epoch 91/500 2/2 [==============================] - 2s 1s/step - loss: 2.4278 - val_loss: 2.1663 Epoch 92/500 2/2 [==============================] - 2s 1s/step - loss: 2.3348 - val_loss: 2.1854 Epoch 93/500 2/2 [==============================] - 2s 1s/step - loss: 2.4526 - val_loss: 2.1720 Epoch 94/500 2/2 [==============================] - 2s 1s/step - loss: 2.4369 - val_loss: 2.1525 Epoch 95/500 2/2 [==============================] - 2s 1s/step - loss: 2.3798 - val_loss: 2.1712 Epoch 96/500 2/2 [==============================] - 2s 1s/step - loss: 2.2422 - val_loss: 2.1950 Epoch 97/500 2/2 [==============================] - 2s 1s/step - loss: 2.3802 - val_loss: 2.1858 Epoch 98/500 2/2 [==============================] - 2s 1s/step - loss: 2.3421 - val_loss: 2.1734 Epoch 99/500 2/2 [==============================] - 2s 1s/step - loss: 2.4127 - val_loss: 2.1725 Epoch 100/500 2/2 [==============================] - 2s 1s/step - loss: 2.5251 - val_loss: 2.1703 Epoch 101/500 2/2 [==============================] - 2s 1s/step - loss: 2.4289 - val_loss: 2.1055 Epoch 102/500 2/2 [==============================] - 2s 1s/step - loss: 2.2184 - val_loss: 2.1307 Epoch 103/500 2/2 [==============================] - 2s 1s/step - loss: 2.3370 - val_loss: 2.1139 Epoch 104/500 2/2 [==============================] - 2s 1s/step - loss: 2.3771 - val_loss: 2.1215 Epoch 105/500 2/2 [==============================] - 2s 1s/step - loss: 2.2837 - val_loss: 2.1227 Epoch 106/500 2/2 [==============================] - 2s 1s/step - loss: 2.4465 - val_loss: 2.1591 Epoch 107/500 2/2 [==============================] - 2s 1s/step - loss: 2.2460 - val_loss: 2.2001 Epoch 108/500 2/2 [==============================] - 2s 1s/step - loss: 2.4187 - val_loss: 2.1326 Epoch 109/500 2/2 [==============================] - 2s 1s/step - loss: 2.2078 - val_loss: 2.0857 Epoch 110/500 2/2 [==============================] - 2s 1s/step - loss: 2.3261 - val_loss: 2.0776 Epoch 111/500 2/2 [==============================] - 2s 1s/step - loss: 2.1122 - val_loss: 2.1018 Epoch 112/500 2/2 [==============================] - 2s 1s/step - loss: 2.2437 - val_loss: 2.0927 Epoch 113/500 2/2 [==============================] - 2s 1s/step - loss: 2.2254 - val_loss: 2.1330 Epoch 114/500 2/2 [==============================] - 2s 1s/step - loss: 2.2168 - val_loss: 2.1278 Epoch 115/500 2/2 [==============================] - 2s 1s/step - loss: 2.5092 - val_loss: 2.1548 Epoch 116/500 2/2 [==============================] - 2s 1s/step - loss: 2.3083 - val_loss: 2.1718 Epoch 117/500 2/2 [==============================] - 2s 1s/step - loss: 2.1969 - val_loss: 2.1931 Epoch 118/500 2/2 [==============================] - 2s 1s/step - loss: 2.2925 - val_loss: 2.1866 Epoch 119/500 2/2 [==============================] - 2s 1s/step - loss: 2.2946 - val_loss: 2.2156 Epoch 120/500 2/2 [==============================] - 2s 1s/step - loss: 2.1715 - val_loss: 2.1717 Epoch 121/500 2/2 [==============================] - 2s 1s/step - loss: 2.2279 - val_loss: 2.1322 Epoch 122/500 2/2 [==============================] - 2s 1s/step - loss: 2.2047 - val_loss: 2.1329 Epoch 123/500 2/2 [==============================] - 2s 1s/step - loss: 2.3536 - val_loss: 2.1313 Epoch 124/500 2/2 [==============================] - 2s 1s/step - loss: 2.2859 - val_loss: 2.1349 Epoch 125/500 2/2 [==============================] - 2s 1s/step - loss: 2.3044 - val_loss: 2.1103 Epoch 126/500 2/2 [==============================] - 2s 1s/step - loss: 2.2274 - val_loss: 2.0960 Epoch 127/500 2/2 [==============================] - 2s 1s/step - loss: 2.3254 - val_loss: 2.0679 Epoch 128/500 2/2 [==============================] - 2s 1s/step - loss: 2.2295 - val_loss: 2.0613 Epoch 129/500 2/2 [==============================] - 2s 1s/step - loss: 2.2567 - val_loss: 2.0961 Epoch 130/500 2/2 [==============================] - 2s 1s/step - loss: 2.2248 - val_loss: 2.1020 Epoch 131/500 2/2 [==============================] - 2s 1s/step - loss: 2.3120 - val_loss: 2.0875 Epoch 132/500 2/2 [==============================] - 2s 1s/step - loss: 2.1721 - val_loss: 2.0671 Epoch 133/500 2/2 [==============================] - 2s 1s/step - loss: 2.2330 - val_loss: 2.0250 Epoch 134/500 2/2 [==============================] - 2s 1s/step - loss: 2.2384 - val_loss: 2.0041 Epoch 135/500 2/2 [==============================] - 2s 1s/step - loss: 2.4626 - val_loss: 2.0028 Epoch 136/500 2/2 [==============================] - 2s 1s/step - loss: 2.2308 - val_loss: 1.9807 Epoch 137/500 2/2 [==============================] - 2s 1s/step - loss: 2.1524 - val_loss: 1.9537 Epoch 138/500 2/2 [==============================] - 2s 1s/step - loss: 2.1594 - val_loss: 1.9875 Epoch 139/500 2/2 [==============================] - 2s 1s/step - loss: 2.2939 - val_loss: 1.9529 Epoch 140/500 2/2 [==============================] - 2s 1s/step - loss: 2.3088 - val_loss: 1.9900 Epoch 141/500 2/2 [==============================] - 2s 1s/step - loss: 2.0643 - val_loss: 2.0214 Epoch 142/500 2/2 [==============================] - 2s 1s/step - loss: 2.2581 - val_loss: 2.0490 Epoch 143/500 2/2 [==============================] - 2s 1s/step - loss: 2.2208 - val_loss: 2.0266 Epoch 144/500 2/2 [==============================] - 2s 1s/step - loss: 2.1797 - val_loss: 2.0536 Epoch 145/500 2/2 [==============================] - 2s 1s/step - loss: 2.3738 - val_loss: 2.0444 Epoch 146/500 2/2 [==============================] - 2s 1s/step - loss: 2.1871 - val_loss: 2.0374 Epoch 147/500 2/2 [==============================] - 2s 1s/step - loss: 2.1942 - val_loss: 2.0384 Epoch 148/500 2/2 [==============================] - 2s 1s/step - loss: 2.3258 - val_loss: 1.9961 Epoch 149/500 2/2 [==============================] - 2s 1s/step - loss: 2.1712 - val_loss: 1.9505 Epoch 150/500 2/2 [==============================] - 2s 1s/step - loss: 2.3734 - val_loss: 1.9826 Epoch 151/500 2/2 [==============================] - 2s 1s/step - loss: 2.2786 - val_loss: 1.9588 Epoch 152/500 2/2 [==============================] - 2s 1s/step - loss: 2.1934 - val_loss: 1.9762 Epoch 153/500 2/2 [==============================] - 2s 1s/step - loss: 2.1712 - val_loss: 1.9921 Epoch 154/500 2/2 [==============================] - 2s 1s/step - loss: 2.1263 - val_loss: 1.9915 Epoch 155/500 2/2 [==============================] - 2s 1s/step - loss: 2.3680 - val_loss: 1.9857 Epoch 156/500 2/2 [==============================] - 2s 1s/step - loss: 2.1209 - val_loss: 1.9903 Epoch 157/500 2/2 [==============================] - 2s 1s/step - loss: 2.1403 - val_loss: 1.9824 Epoch 158/500 2/2 [==============================] - 2s 1s/step - loss: 2.1296 - val_loss: 1.9917 Epoch 159/500 2/2 [==============================] - 2s 1s/step - loss: 2.0997 - val_loss: 1.9922 Epoch 160/500 2/2 [==============================] - 2s 1s/step - loss: 2.1667 - val_loss: 2.0005 Epoch 161/500 2/2 [==============================] - 2s 1s/step - loss: 2.1397 - val_loss: 2.0044 Epoch 162/500 2/2 [==============================] - 2s 1s/step - loss: 2.0623 - val_loss: 1.9753 Epoch 163/500 2/2 [==============================] - 2s 1s/step - loss: 2.0442 - val_loss: 1.9606 Epoch 164/500 2/2 [==============================] - 2s 1s/step - loss: 2.0807 - val_loss: 1.9689 Epoch 165/500 2/2 [==============================] - 2s 1s/step - loss: 2.1479 - val_loss: 1.9738 Epoch 166/500 2/2 [==============================] - 2s 1s/step - loss: 2.1212 - val_loss: 1.9840 Epoch 167/500 2/2 [==============================] - 2s 1s/step - loss: 2.2002 - val_loss: 1.9763 Epoch 168/500 2/2 [==============================] - 2s 1s/step - loss: 2.1082 - val_loss: 1.9784 Epoch 169/500 2/2 [==============================] - 3s 2s/step - loss: 2.1880 - val_loss: 1.9558 Epoch 169: early stopping Epoch 1/500 6/6 [==============================] - 36s 758ms/step - loss: 3.6916 - val_loss: 2.0184 Epoch 2/500 6/6 [==============================] - 3s 409ms/step - loss: 3.1390 - val_loss: 2.2699 Epoch 3/500 6/6 [==============================] - 3s 408ms/step - loss: 2.6015 - val_loss: 2.3716 Epoch 4/500 6/6 [==============================] - 3s 412ms/step - loss: 2.5459 - val_loss: 2.4933 Epoch 5/500 6/6 [==============================] - 3s 400ms/step - loss: 2.2372 - val_loss: 2.4185 Epoch 6/500 6/6 [==============================] - 3s 458ms/step - loss: 2.2382 - val_loss: 2.4953 Epoch 7/500 6/6 [==============================] - 3s 439ms/step - loss: 2.0820 - val_loss: 2.3434 Epoch 8/500 6/6 [==============================] - 3s 405ms/step - loss: 1.9963 - val_loss: 2.3684 Epoch 9/500 6/6 [==============================] - 3s 436ms/step - loss: 2.0033 - val_loss: 2.3100 Epoch 10/500 6/6 [==============================] - 3s 444ms/step - loss: 1.8193 - val_loss: 2.2781 Epoch 11/500 6/6 [==============================] - 3s 393ms/step - loss: 2.1569 - val_loss: 2.3323 Epoch 12/500 6/6 [==============================] - 3s 402ms/step - loss: 1.9054 - val_loss: 2.3388 Epoch 13/500 6/6 [==============================] - 3s 431ms/step - 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loss: 2.3934 - val_loss: 2.1902 Epoch 78/500 6/6 [==============================] - 2s 396ms/step - loss: 2.4712 - val_loss: 2.1684 Epoch 79/500 6/6 [==============================] - 2s 402ms/step - loss: 2.5838 - val_loss: 2.1529 Epoch 80/500 6/6 [==============================] - 2s 390ms/step - loss: 2.8386 - val_loss: 2.1860 Epoch 81/500 6/6 [==============================] - 2s 399ms/step - loss: 2.5576 - val_loss: 2.1339 Epoch 82/500 6/6 [==============================] - 2s 393ms/step - loss: 2.5192 - val_loss: 2.1020 Epoch 83/500 6/6 [==============================] - 2s 383ms/step - loss: 2.6917 - val_loss: 2.1269 Epoch 84/500 6/6 [==============================] - 2s 387ms/step - loss: 2.5673 - val_loss: 2.1247 Epoch 85/500 6/6 [==============================] - 2s 396ms/step - loss: 2.6316 - val_loss: 2.1411 Epoch 86/500 6/6 [==============================] - 2s 414ms/step - loss: 2.4855 - val_loss: 2.1826 Epoch 87/500 6/6 [==============================] - 2s 399ms/step - loss: 2.5176 - val_loss: 2.2080 Epoch 88/500 6/6 [==============================] - 2s 384ms/step - loss: 2.5566 - val_loss: 2.1476 Epoch 89/500 6/6 [==============================] - 2s 393ms/step - loss: 2.4796 - val_loss: 2.1838 Epoch 90/500 6/6 [==============================] - 2s 390ms/step - loss: 2.4382 - val_loss: 2.0925 Epoch 91/500 6/6 [==============================] - 3s 441ms/step - loss: 2.5248 - val_loss: 2.0865 Epoch 92/500 6/6 [==============================] - 3s 443ms/step - loss: 2.5292 - val_loss: 2.0802 Epoch 93/500 6/6 [==============================] - 3s 408ms/step - loss: 2.6416 - val_loss: 2.0762 Epoch 94/500 6/6 [==============================] - 2s 393ms/step - loss: 2.3843 - val_loss: 2.0787 Epoch 95/500 6/6 [==============================] - 3s 423ms/step - loss: 2.3888 - val_loss: 2.0611 Epoch 96/500 6/6 [==============================] - 2s 393ms/step - loss: 2.6759 - val_loss: 2.1151 Epoch 97/500 6/6 [==============================] - 2s 387ms/step - loss: 2.4557 - val_loss: 2.0788 Epoch 98/500 6/6 [==============================] - 3s 440ms/step - loss: 2.4592 - val_loss: 2.0400 Epoch 99/500 6/6 [==============================] - 3s 437ms/step - loss: 2.3528 - val_loss: 2.0325 Epoch 100/500 6/6 [==============================] - 3s 443ms/step - loss: 2.3535 - val_loss: 2.0148 Epoch 101/500 6/6 [==============================] - 3s 430ms/step - loss: 2.6083 - val_loss: 2.0079 Epoch 102/500 6/6 [==============================] - 3s 444ms/step - loss: 2.4150 - val_loss: 1.9917 Epoch 103/500 6/6 [==============================] - 2s 361ms/step - loss: 2.5875 - val_loss: 2.0394 Epoch 104/500 6/6 [==============================] - 2s 396ms/step - loss: 2.6018 - val_loss: 2.0633 Epoch 105/500 6/6 [==============================] - 2s 389ms/step - loss: 2.3862 - val_loss: 2.0270 Epoch 106/500 6/6 [==============================] - 2s 396ms/step - loss: 2.5674 - val_loss: 2.0605 Epoch 107/500 6/6 [==============================] - 2s 374ms/step - loss: 2.4243 - val_loss: 2.0424 Epoch 108/500 6/6 [==============================] - 2s 396ms/step - loss: 2.4600 - val_loss: 2.0279 Epoch 109/500 6/6 [==============================] - 2s 377ms/step - loss: 2.4665 - val_loss: 2.0110 Epoch 110/500 6/6 [==============================] - 2s 389ms/step - loss: 2.5692 - val_loss: 2.0907 Epoch 111/500 6/6 [==============================] - 2s 402ms/step - loss: 2.4805 - val_loss: 2.0685 Epoch 112/500 6/6 [==============================] - 2s 397ms/step - loss: 2.5106 - val_loss: 2.0451 Epoch 113/500 6/6 [==============================] - 2s 386ms/step - loss: 2.4530 - val_loss: 2.0344 Epoch 114/500 6/6 [==============================] - 2s 392ms/step - loss: 2.4646 - val_loss: 2.0107 Epoch 115/500 6/6 [==============================] - 3s 447ms/step - loss: 2.5858 - val_loss: 1.9901 Epoch 116/500 6/6 [==============================] - 2s 374ms/step - loss: 2.5665 - val_loss: 2.0009 Epoch 117/500 6/6 [==============================] - 3s 434ms/step - loss: 2.3654 - val_loss: 1.9818 Epoch 118/500 6/6 [==============================] - 2s 377ms/step - loss: 2.4967 - val_loss: 2.0197 Epoch 119/500 6/6 [==============================] - 2s 390ms/step - loss: 2.6798 - val_loss: 2.0174 Epoch 120/500 6/6 [==============================] - 2s 390ms/step - loss: 2.3989 - val_loss: 2.0133 Epoch 121/500 6/6 [==============================] - 2s 383ms/step - loss: 2.4209 - val_loss: 2.0556 Epoch 122/500 6/6 [==============================] - 2s 399ms/step - loss: 2.5805 - val_loss: 2.0375 Epoch 123/500 6/6 [==============================] - 2s 396ms/step - loss: 2.5146 - val_loss: 2.0161 Epoch 124/500 6/6 [==============================] - 2s 377ms/step - loss: 2.2460 - val_loss: 2.0574 Epoch 125/500 6/6 [==============================] - 2s 393ms/step - loss: 2.4696 - val_loss: 2.0866 Epoch 126/500 6/6 [==============================] - 2s 383ms/step - loss: 2.2042 - val_loss: 2.1058 Epoch 127/500 6/6 [==============================] - 2s 374ms/step - loss: 2.4027 - val_loss: 2.0825 Epoch 128/500 6/6 [==============================] - 3s 399ms/step - loss: 2.6062 - val_loss: 2.0867 Epoch 129/500 6/6 [==============================] - 2s 380ms/step - loss: 2.3816 - val_loss: 2.0163 Epoch 130/500 6/6 [==============================] - 2s 393ms/step - loss: 2.4744 - val_loss: 2.0451 Epoch 131/500 6/6 [==============================] - 2s 393ms/step - loss: 2.5002 - val_loss: 2.0774 Epoch 132/500 6/6 [==============================] - 2s 393ms/step - loss: 2.4241 - val_loss: 2.0511 Epoch 133/500 6/6 [==============================] - 2s 396ms/step - loss: 2.4562 - val_loss: 2.0736 Epoch 134/500 6/6 [==============================] - 2s 393ms/step - loss: 2.4903 - val_loss: 2.0825 Epoch 135/500 6/6 [==============================] - 2s 399ms/step - loss: 2.2260 - val_loss: 2.0881 Epoch 136/500 6/6 [==============================] - 2s 402ms/step - loss: 2.2589 - val_loss: 2.0462 Epoch 137/500 6/6 [==============================] - 3s 553ms/step - loss: 2.5452 - val_loss: 2.1046 Epoch 137: early stopping Epoch 1/500 6/6 [==============================] - 36s 839ms/step - loss: 3.8495 - val_loss: 2.0275 Epoch 2/500 6/6 [==============================] - 3s 480ms/step - loss: 3.0978 - val_loss: 2.1857 Epoch 3/500 6/6 [==============================] - 3s 485ms/step - loss: 2.7500 - val_loss: 2.2092 Epoch 4/500 6/6 [==============================] - 3s 458ms/step - loss: 2.4147 - val_loss: 2.2556 Epoch 5/500 6/6 [==============================] - 3s 499ms/step - loss: 2.3354 - val_loss: 2.3570 Epoch 6/500 6/6 [==============================] - 3s 518ms/step - loss: 2.3358 - val_loss: 2.3474 Epoch 7/500 6/6 [==============================] - 3s 411ms/step - loss: 2.1514 - val_loss: 2.4214 Epoch 8/500 6/6 [==============================] - 3s 538ms/step - loss: 2.1058 - val_loss: 2.2842 Epoch 9/500 6/6 [==============================] - 3s 531ms/step - loss: 2.0881 - val_loss: 2.2195 Epoch 10/500 6/6 [==============================] - 3s 540ms/step - loss: 1.9913 - val_loss: 2.1579 Epoch 11/500 6/6 [==============================] - 3s 470ms/step - loss: 1.8933 - val_loss: 2.1901 Epoch 12/500 6/6 [==============================] - 3s 493ms/step - loss: 1.8902 - val_loss: 2.1770 Epoch 13/500 6/6 [==============================] - 3s 499ms/step - loss: 1.8652 - val_loss: 2.2916 Epoch 14/500 6/6 [==============================] - 3s 491ms/step - loss: 1.7773 - val_loss: 2.2418 Epoch 15/500 6/6 [==============================] - 3s 543ms/step - loss: 1.8376 - val_loss: 2.0604 Epoch 16/500 6/6 [==============================] - 3s 493ms/step - loss: 1.8046 - val_loss: 2.1412 Epoch 17/500 6/6 [==============================] - 3s 527ms/step - loss: 1.7557 - val_loss: 2.1860 Epoch 18/500 6/6 [==============================] - 3s 481ms/step - loss: 1.6431 - val_loss: 2.1475 Epoch 19/500 6/6 [==============================] - 4s 552ms/step - loss: 1.8616 - val_loss: 1.9854 Epoch 20/500 6/6 [==============================] - 3s 537ms/step - loss: 1.7426 - val_loss: 1.8815 Epoch 21/500 6/6 [==============================] - 3s 558ms/step - loss: 1.6734 - val_loss: 1.8227 Epoch 22/500 6/6 [==============================] - 4s 565ms/step - loss: 1.7639 - val_loss: 1.7845 Epoch 23/500 6/6 [==============================] - 3s 555ms/step - loss: 1.6731 - val_loss: 1.7305 Epoch 24/500 6/6 [==============================] - 3s 498ms/step - loss: 1.6735 - val_loss: 1.7640 Epoch 25/500 6/6 [==============================] - 4s 549ms/step - loss: 1.6349 - val_loss: 1.7003 Epoch 26/500 6/6 [==============================] - 4s 534ms/step - loss: 1.7442 - val_loss: 1.6971 Epoch 27/500 6/6 [==============================] - 3s 532ms/step - loss: 1.6380 - val_loss: 1.6940 Epoch 28/500 6/6 [==============================] - 3s 540ms/step - loss: 1.5586 - val_loss: 1.6744 Epoch 29/500 6/6 [==============================] - 3s 490ms/step - loss: 1.5031 - val_loss: 1.6918 Epoch 30/500 6/6 [==============================] - 3s 496ms/step - loss: 1.6260 - val_loss: 1.6932 Epoch 31/500 6/6 [==============================] - 3s 542ms/step - loss: 1.6304 - val_loss: 1.6374 Epoch 32/500 6/6 [==============================] - 4s 553ms/step - loss: 1.6018 - val_loss: 1.6264 Epoch 33/500 6/6 [==============================] - 3s 530ms/step - loss: 1.5496 - val_loss: 1.6033 Epoch 34/500 6/6 [==============================] - 4s 546ms/step - loss: 1.5898 - val_loss: 1.5840 Epoch 35/500 6/6 [==============================] - 4s 574ms/step - loss: 1.5664 - val_loss: 1.5710 Epoch 36/500 6/6 [==============================] - 3s 547ms/step - loss: 1.4181 - val_loss: 1.5616 Epoch 37/500 6/6 [==============================] - 3s 488ms/step - loss: 1.4616 - val_loss: 1.5694 Epoch 38/500 6/6 [==============================] - 3s 526ms/step - loss: 1.4490 - val_loss: 1.5520 Epoch 39/500 6/6 [==============================] - 3s 545ms/step - loss: 1.4618 - val_loss: 1.5391 Epoch 40/500 6/6 [==============================] - 3s 546ms/step - loss: 1.4209 - val_loss: 1.5272 Epoch 41/500 6/6 [==============================] - 3s 487ms/step - loss: 1.5632 - val_loss: 1.5352 Epoch 42/500 6/6 [==============================] - 3s 518ms/step - loss: 1.5052 - val_loss: 1.5667 Epoch 43/500 6/6 [==============================] - 3s 499ms/step - loss: 1.4094 - val_loss: 1.5449 Epoch 44/500 6/6 [==============================] - 3s 504ms/step - loss: 1.5029 - val_loss: 1.5432 Epoch 45/500 6/6 [==============================] - 3s 485ms/step - loss: 1.5680 - val_loss: 1.5855 Epoch 46/500 6/6 [==============================] - 3s 490ms/step - loss: 1.4601 - val_loss: 1.5934 Epoch 47/500 6/6 [==============================] - 3s 499ms/step - loss: 1.4187 - val_loss: 1.6364 Epoch 48/500 6/6 [==============================] - 3s 509ms/step - loss: 1.4143 - val_loss: 1.6036 Epoch 49/500 6/6 [==============================] - 3s 515ms/step - loss: 1.4475 - val_loss: 1.5868 Epoch 50/500 6/6 [==============================] - 3s 499ms/step - loss: 1.3621 - val_loss: 1.5466 Epoch 51/500 6/6 [==============================] - 3s 532ms/step - loss: 1.3418 - val_loss: 1.5049 Epoch 52/500 6/6 [==============================] - 4s 535ms/step - loss: 1.3310 - val_loss: 1.5044 Epoch 53/500 6/6 [==============================] - 4s 537ms/step - loss: 1.3894 - val_loss: 1.4924 Epoch 54/500 6/6 [==============================] - 4s 533ms/step - loss: 1.4941 - val_loss: 1.4827 Epoch 55/500 6/6 [==============================] - 3s 536ms/step - loss: 1.3969 - val_loss: 1.4700 Epoch 56/500 6/6 [==============================] - 3s 533ms/step - loss: 1.3532 - val_loss: 1.4689 Epoch 57/500 6/6 [==============================] - 3s 484ms/step - loss: 1.5459 - val_loss: 1.5045 Epoch 58/500 6/6 [==============================] - 3s 512ms/step - loss: 1.4863 - val_loss: 1.5093 Epoch 59/500 6/6 [==============================] - 3s 506ms/step - loss: 1.4735 - val_loss: 1.5006 Epoch 60/500 6/6 [==============================] - 3s 489ms/step - loss: 1.3647 - val_loss: 1.5024 Epoch 61/500 6/6 [==============================] - 3s 508ms/step - loss: 1.4201 - val_loss: 1.5060 Epoch 62/500 6/6 [==============================] - 3s 510ms/step - loss: 1.4169 - val_loss: 1.5140 Epoch 63/500 6/6 [==============================] - 3s 498ms/step - loss: 1.3858 - val_loss: 1.5293 Epoch 64/500 6/6 [==============================] - 3s 493ms/step - loss: 1.3843 - val_loss: 1.5201 Epoch 65/500 6/6 [==============================] - 3s 513ms/step - loss: 1.4739 - val_loss: 1.5128 Epoch 66/500 6/6 [==============================] - 3s 503ms/step - loss: 1.4552 - val_loss: 1.4873 Epoch 67/500 6/6 [==============================] - 3s 495ms/step - loss: 1.3109 - val_loss: 1.5384 Epoch 68/500 6/6 [==============================] - 3s 501ms/step - loss: 1.4278 - val_loss: 1.5339 Epoch 69/500 6/6 [==============================] - 3s 509ms/step - loss: 1.2426 - val_loss: 1.5372 Epoch 70/500 6/6 [==============================] - 3s 503ms/step - loss: 1.3780 - val_loss: 1.5087 Epoch 71/500 6/6 [==============================] - 3s 509ms/step - loss: 1.3739 - val_loss: 1.4989 Epoch 72/500 6/6 [==============================] - 3s 496ms/step - loss: 1.3250 - val_loss: 1.5709 Epoch 73/500 6/6 [==============================] - 3s 502ms/step - loss: 1.3878 - val_loss: 1.6043 Epoch 74/500 6/6 [==============================] - 3s 507ms/step - loss: 1.3733 - val_loss: 1.5484 Epoch 75/500 6/6 [==============================] - 3s 492ms/step - loss: 1.3218 - val_loss: 1.5305 Epoch 76/500 6/6 [==============================] - 5s 824ms/step - loss: 1.3063 - val_loss: 1.5779 Epoch 76: early stopping
#Get predictions for validation and training sets
validate_pred = np.zeros((nEnsemble, nTest, nDim))
test_pred = np.zeros((nEnsemble, nTest, nDim))
rocks_120_pred = np.zeros((nEnsemble, 120, nDim))
for e in range(nEnsemble):
model = load_model("ensemble_densenet_8_{}.hdf5".format(e))
validate_pred[e,:] = model.predict(X_validate)
test_pred[e,:] = model.predict(X_test)
rocks_120_pred[e,:] = model.predict(X_120)
K.clear_session()
validate_prediction = np.mean(validate_pred, 0)
test_prediction = np.mean(test_pred, 0)
rocks_120_prediction = np.mean(rocks_120_pred, 0)
#Get MSE
print(mean_squared_error(Y_validate, validate_prediction))
print(mean_squared_error(Y_test, test_prediction))
print(mean_squared_error(Y_120, rocks_120_prediction))
3/3 [==============================] - 2s 189ms/step 3/3 [==============================] - 0s 70ms/step 4/4 [==============================] - 0s 63ms/step 3/3 [==============================] - 2s 157ms/step 3/3 [==============================] - 0s 71ms/step 4/4 [==============================] - 0s 63ms/step 3/3 [==============================] - 2s 236ms/step 3/3 [==============================] - 0s 69ms/step 4/4 [==============================] - 0s 61ms/step 3/3 [==============================] - 3s 236ms/step 3/3 [==============================] - 0s 63ms/step 4/4 [==============================] - 0s 68ms/step 3/3 [==============================] - 2s 228ms/step 3/3 [==============================] - 0s 63ms/step 4/4 [==============================] - 0s 59ms/step 3/3 [==============================] - 2s 212ms/step 3/3 [==============================] - 0s 63ms/step 4/4 [==============================] - 0s 59ms/step 3/3 [==============================] - 2s 216ms/step 3/3 [==============================] - 0s 63ms/step 4/4 [==============================] - 0s 58ms/step 3/3 [==============================] - 2s 220ms/step 3/3 [==============================] - 0s 63ms/step 4/4 [==============================] - 0s 59ms/step 3/3 [==============================] - 2s 197ms/step 3/3 [==============================] - 0s 63ms/step 4/4 [==============================] - 0s 60ms/step 3/3 [==============================] - 2s 157ms/step 3/3 [==============================] - 0s 71ms/step 4/4 [==============================] - 0s 63ms/step 1.3152003843531315 1.4433026833068152 3.0617280031522824
#Get R2
print(r2_score(Y_validate, validate_prediction))
print(r2_score(Y_test, test_prediction))
print(r2_score(Y_120, rocks_120_prediction))
0.7687254736329209 0.7525643698572588 -0.41798612086330167
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from PIL import Image
images = X_validate
mds_coordinates = validate_prediction
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8)
scaled_images = np.clip(scaled_images, 0, 255)
# Create subplots for different pairs of dimensions
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
# Pairs of dimensions: (0, 1), (2, 3), (4, 5), (6, 7)
dimension_pairs = [(0, 1), (2, 3), (4, 5), (6, 7)]
for i, ax in enumerate(axes.flatten()):
dimension_x, dimension_y = dimension_pairs[i]
# Scatter plot of MDS coordinates
ax.scatter(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y], c='blue', alpha=0.5)
# Function to display resized images
def plot_resized_image(img, x, y, ax):
img_copy = Image.fromarray(img)
img_copy = img_copy.resize((20, 20)) # Resize images to fit within the subplot
imagebox = OffsetImage(img_copy, zoom=1.0)
ab = AnnotationBbox(imagebox, (x, y), frameon=False)
ax.add_artist(ab)
# Plot each image at its respective MDS coordinates
for j, (x, y) in enumerate(zip(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y])):
image = scaled_images[j] # Get the image at index j
plot_resized_image(image, x, y, ax)
ax.set_xlabel(f'MDS Dimension {dimension_x+1}')
ax.set_ylabel(f'MDS Dimension {dimension_y+1}')
plt.suptitle(f'Visualization of CNN predicted MDS Dimensions')
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from PIL import Image
images = X_test
mds_coordinates = test_prediction
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8)
scaled_images = np.clip(scaled_images, 0, 255)
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
dimension_pairs = [(0, 1), (2, 3), (4, 5), (6, 7)]
for i, ax in enumerate(axes.flatten()):
dimension_x, dimension_y = dimension_pairs[i]
# Scatter plot of MDS coordinates
ax.scatter(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y], c='blue', alpha=0.5)
# Function to display resized images
def plot_resized_image(img, x, y, ax):
img_copy = Image.fromarray(img)
img_copy = img_copy.resize((20, 20)) # Resize images to fit within the subplot
imagebox = OffsetImage(img_copy, zoom=1.0)
ab = AnnotationBbox(imagebox, (x, y), frameon=False)
ax.add_artist(ab)
# Plot each image at its respective MDS coordinates
for j, (x, y) in enumerate(zip(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y])):
image = scaled_images[j] # Get the image at index j
plot_resized_image(image, x, y, ax)
ax.set_xlabel(f'MDS Dimension {dimension_x+1}')
ax.set_ylabel(f'MDS Dimension {dimension_y+1}')
plt.suptitle(f'Visualization of CNN predicted MDS Dimensions')
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from PIL import Image
images = X_120
mds_coordinates = rocks_120_prediction
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8)
scaled_images = np.clip(scaled_images, 0, 255)
# Create subplots for different pairs of dimensions
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
# Pairs of dimensions: (0, 1), (2, 3), (4, 5), (6, 7)
dimension_pairs = [(0, 1), (2, 3), (4, 5), (6, 7)]
for i, ax in enumerate(axes.flatten()):
dimension_x, dimension_y = dimension_pairs[i]
# Scatter plot of MDS coordinates
ax.scatter(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y], c='blue', alpha=0.5)
# Function to display resized images
def plot_resized_image(img, x, y, ax):
img_copy = Image.fromarray(img)
img_copy = img_copy.resize((20, 20)) # Resize images to fit within the subplot
imagebox = OffsetImage(img_copy, zoom=1.0)
ab = AnnotationBbox(imagebox, (x, y), frameon=False)
ax.add_artist(ab)
# Plot each image at its respective MDS coordinates
for j, (x, y) in enumerate(zip(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y])):
image = scaled_images[j] # Get the image at index j
plot_resized_image(image, x, y, ax)
ax.set_xlabel(f'MDS Dimension {dimension_x+1}')
ax.set_ylabel(f'MDS Dimension {dimension_y+1}')
plt.suptitle(f'Visualization of CNN predicted MDS Dimensions')
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from scipy.stats import pearsonr
from PIL import Image
images = X_validate
mds_cnn_coordinates = validate_prediction
mds_actual_coordinates = Y_validate
# Rescale images
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8)
scaled_images = np.clip(scaled_images, 0, 255)
# Calculate Pearson correlation coefficients for each dimension
correlation_coefficients = [pearsonr(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])[0] for i in range(8)]
# Create subplots for each dimension
fig, axes = plt.subplots(4, 2, figsize=(12, 20))
# fig.suptitle("Scatterplots of CNN-predicted dimensions against MDS derived dimensions")
axes = axes.flatten()
for i in range(8):
ax = axes[i]
ax.scatter(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i], alpha=0.7)
for j, (x_cnn, y_actual) in enumerate(zip(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])):
plot_resized_image(scaled_images[j], x_cnn, y_actual, ax)
ax.plot(np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
'k--', label='Perfect Prediction')
ax.set_xlabel(f'CNN-predicted Dimension {i+1}')
ax.set_ylabel(f'MDS-derived Dimension {i+1}')
ax.legend()
ax.grid(True)
ax.text(0.5, 0.95, f"r: {correlation_coefficients[i]:.4f}", ha='center', va='center', transform=ax.transAxes, fontsize=8)
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from scipy.stats import pearsonr
from PIL import Image
images = X_test
mds_cnn_coordinates = test_prediction
mds_actual_coordinates = Y_test
# Rescale images
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8)
scaled_images = np.clip(scaled_images, 0, 255)
# Calculate Pearson correlation coefficients for each dimension
correlation_coefficients = [pearsonr(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])[0] for i in range(8)]
# Create subplots for each dimension
fig, axes = plt.subplots(4, 2, figsize=(12, 20))
axes = axes.flatten()
for i in range(8):
ax = axes[i]
ax.scatter(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i], alpha=0.7)
for j, (x_cnn, y_actual) in enumerate(zip(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])):
plot_resized_image(scaled_images[j], x_cnn, y_actual, ax)
ax.plot(np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
'k--', label='Perfect Prediction')
ax.set_xlabel(f'CNN-predicted Dimension {i+1}')
ax.set_ylabel(f'MDS-derived Dimension {i+1}')
ax.legend()
ax.grid(True)
ax.text(0.5, 0.95, f"r: {correlation_coefficients[i]:.4f}", ha='center', va='center', transform=ax.transAxes, fontsize=8)
plt.tight_layout()
# plt.suptitle("Scatterplots of CNN-predicted dimensions against MDS derived dimensions")
plt.show()
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from scipy.stats import pearsonr
from PIL import Image
images = X_120
mds_cnn_coordinates = rocks_120_prediction
mds_actual_coordinates = Y_120
# Rescale images
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8)
scaled_images = np.clip(scaled_images, 0, 255)
# Calculate Pearson correlation coefficients for each dimension
correlation_coefficients = [pearsonr(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])[0] for i in range(8)]
# Create subplots for each dimension
fig, axes = plt.subplots(4, 2, figsize=(12, 20))
# fig.suptitle("Scatterplots of CNN-predicted dimensions against MDS derived dimensions")
axes = axes.flatten()
for i in range(8):
ax = axes[i]
ax.scatter(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i], alpha=0.7)
for j, (x_cnn, y_actual) in enumerate(zip(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])):
plot_resized_image(scaled_images[j], x_cnn, y_actual, ax)
ax.plot(np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
'k--', label='Perfect Prediction')
ax.set_xlabel(f'CNN-predicted Dimension {i+1}')
ax.set_ylabel(f'MDS-derived Dimension {i+1}')
ax.legend()
ax.grid(True)
ax.text(0.5, 0.95, f"r: {correlation_coefficients[i]:.4f}", ha='center', va='center', transform=ax.transAxes, fontsize=8)
plt.tight_layout()
plt.show()